TimeFound: A Foundation Model for Time Series Forecasting
This addresses the need for out-of-the-box forecasting tools for practitioners in various domains, though it appears incremental as it builds on existing foundation model approaches.
The paper tackles the problem of zero-shot forecasting across diverse time series domains by introducing TimeFound, a transformer-based foundation model, which achieves superior or competitive performance on unseen datasets compared to state-of-the-art models.
We present TimeFound, an encoder-decoder transformer-based time series foundation model for out-of-the-box zero-shot forecasting. To handle time series data from various domains, TimeFound employs a multi-resolution patching strategy to capture complex temporal patterns at multiple scales. We pre-train our model with two sizes (200M and 710M parameters) on a large time-series corpus comprising both real-world and synthetic datasets. Over a collection of unseen datasets across diverse domains and forecasting horizons, our empirical evaluations suggest that TimeFound can achieve superior or competitive zero-shot forecasting performance, compared to state-of-the-art time series foundation models.